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iREAD: a tool for intron retention detection from RNA-seq data.

Hong-Dong Li^1,2, Cory C Funk², Nathan D Price³

¹Center for Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan Province, 410083, People's Republic of China.

|February 8, 2020

Summary

Intron retention (IR) analysis is crucial for understanding gene expression and disease. We developed iREAD, a novel Python tool for accurate genome-wide detection of intron retention events from RNA-seq data.

Keywords:

Gene expression Intron retention RNA-seq iREAD

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Published on: December 9, 2016

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Intron retention (IR) was historically dismissed as transcriptional noise.
Emerging evidence highlights IR's role in gene regulation and association with diseases like cancer.
Current methods for IR detection are limited, necessitating new approaches.

Purpose of the Study:

To develop and present iREAD, a novel computational tool for genome-wide intron retention analysis.
To improve the detection accuracy of intron retention events from high-throughput RNA-seq data.

Main Methods:

iREAD is a command-line Python tool that processes BAM files and genome intron coordinates.
It quantifies reads overlapping introns and analyzes read depth and distribution for IR event detection.
Outputs a list of retained introns in a tab-delimited format.

Main Results:

iREAD demonstrates superior performance in detecting IR events compared to existing tools like IRFinder, evidenced by higher AUC values.
Both iREAD and IRFinder exhibit low false positive rates but high false negative rates, suggesting complementary utility.
The output facilitates downstream analyses such as differential intron expression and functional enrichment.

Conclusions:

iREAD offers a valuable, generic tool for interrogating intron regions in poly-A enriched transcriptomic data.
Intron retention analysis provides a complementary perspective for comprehensive transcriptome understanding.